Robotic and document analysis cross-fertilization: Improving place cells based robot navigation

This paper proposes a place cell model allowing place recognition in the context of robot autonomous navigation. The robustness of this approach lies in the fact that even if one or several patterns characterizing the place are removed or not visible anymore, a place can still be recognized. The recognition process in this work is improved with respect to the state-of-the-art place cells approach. Additionally, the interconnection of the modules is made such that the robot is able to learn new places as it navigates and interacts with the environment to get to its final destination. Experimental results validate the advantage of the incremental learning allowing the robot to cope with any unforeseen changes and thus adapting itself to the environment.